Cognitive Beamforming in Underlay Two-Way Relay Networks With Multiantenna Terminals
Bibliographic record
Abstract
This paper studies an underlay cognitive network consisting of a two-way amplify-and-forward (AF) relay and two multiantenna terminals (SU1and SU2). Despite enhanced spectral efficiency and spectrum utilization, the underlay network is limited by low power transmissions and short coverage owing to secondary-to-primary (S2P) and primary-to-secondary (P2S) interference. To alleviate these, we consider beamforming at SU1and SU2. However, concurrent bidirectional transmissions with the two-way relay complicates beamforming and power allocation. Nevertheless, we use the performance criterion of maximizing the worse received signal-to-interference-and-noise ratio (SINR) at SU1and SU2. The resulting maximization problem for the optimal beamforming vectors and power allocation is a nonconvex quadratically constraint quadratic program (QCQP), which is NP-hard. Thus, we develop an iterative bisection search, but determining its feasibility at each iteration is still a nonconvex NP-hard QCQP. We thus generate two equivalent interference minimization problems, which we solve by semidefinite relaxation (SDR). Simulation results show that our proposed optimal design improves SINR by as much as 20 dB. We also propose suboptimal maximal-ratio-transmission (MRT) and zero-forcing beamforming and maximal-ratio-transmission (ZFB-MRT), and develop their optimal power allocations. Importantly, the performance loss due to these suboptimal strategies is modest (e.g., as low as 1 dB for ZFB-MRT with optimal power allocation).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".